Machine learning-based techniques to facilitate the production of stone nano powder-reinforced manufactured-sand concrete

Zanyu Huang, Qiuyue Han, Adil Hussein Mohammed, Arsalan Mahmoodzadeh, Nejib Ghazouani, Shtwai Alsubai, Abed Alanazi and Abdullah Alqahtani

Abstract

This study aims to examine four machine learning (ML)-based models for their potential to estimate the splitting tensile strength (STS) of manufactured sand concrete (MSC). The ML models were trained and tested based on 310 experimental data points. Stone nanopowder content (SNPC), curing age (CA), and water-to-cement (W/C) ratio were also studied for their impacts on the STS of MSC. According to the results, the support vector regression (SVR) model had the highest correlation with experimental data. Still, all of the optimized ML models showed promise in estimating the STS of MSC. Both ML and laboratory results showed that MSC with 10% SNPC improved the STS of MSC.

Key Words

machine learning; manufactured-sand concrete; stone nano-powder; tensile strength

Address

Zanyu Huang and Qiuyue Han: College of Economics and Management Engineering, Beijing Institute of Civil Engineering and Architecture, Daxing 102600, Beijing, China Adil Hussein Mohammed: Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq Arsalan Mahmoodzadeh: IRO, Civil Engineering Department, University of Halabja, Halabja, 46018, Iraq Nejib Ghazouani: Department of Civil Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia/ Civil Engineering Laboratory, National Engineers School of Tunis (ENIT), University of Tunis El Manar, Tunis 1002, Tunisia Shtwai Alsubai and Abed Alanazi: Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia Abdullah Alqahtani: Software Engineering Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia

← Back to Volume 15, No. 6